Computerized clinical data banks possess enormous potential as tools for assessing the efficacy of new diagnostic and therapeutic modalities, for monitoring the quality of health care delivery, and for support of basic medical research. Because of this potential, many clinical data banks have recently been developed throughout the United States. However, once the initial problems of data acquisition, storage, and retrieval have been dealt with, there remains a set of complex problems inherent in the task of accurately inferring medical knowledge from a collection of observations in patient records. These problems concern the complexity of disease and outcome definitions, the complexity of time relationships, potential biases in compared subsets, and missing and outlying data. Underlying the proposed research is the hypothesis that reliable knowledge inference may be greatly facilitated by using a medical knowledge base in conjunction with the data bank. Utilizing symbolic processing techniques from artificial intelligence, the knowledge base is a collection of meaningful medical concepts which can be used to interpret the observations of symptoms, therapy, and outcome in the data bank. This process of conceptual abstraction makes subsequent statistical analyses far less sensitive to missing data. Organization of concepts in the knowledge base also provides a mechanism for dealing with bias. We propose to develop a knowledge base/data bank pilot system, called RX, which will utilize the Time-Oriented Database (TOD) System and ARAMIS Databank of Stanford University to address key issues in management of rheumatologic diseases. The system may be generalized to deal with all chronic diseases as a major new methodology for acquisition of knowledge to supplement the role of randomized controlled trials.